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Record W2130480536 · doi:10.1086/368166

High Frequency of Human Herpesvirus 6 DNA in Multiple Sclerosis Plaques Isolated by Laser Microdissection

2003· article· en· W2130480536 on OpenAlexaff
Claudio Cermelli, Rossana Berti, Samantha S. Soldan, Michael Mayne, James M. Dambrosia, Samuel K. Ludwin, Steven Jacobson

Bibliographic record

VenueThe Journal of Infectious Diseases · 2003
Typearticle
Languageen
FieldMedicine
TopicCytomegalovirus and herpesvirus research
Canadian institutionsQueen's UniversityUniversity of Manitoba
Fundersnot available
KeywordsLaser capture microdissectionPathologyMultiple sclerosisPolymerase chain reactionWhite matterDNAHuman herpesvirus 6MicrodissectionAutopsyMedicineBiologyVirusHerpesviridaeVirologyViral diseaseImmunologyGeneMagnetic resonance imaging

Abstract

fetched live from OpenAlex

The frequency of human herpesvirus 6 (HHV-6) DNA was assessed in autopsy material from multiple sclerosis (MS) plaques and normal-appearing white matter (NAWM) from brains of persons with MS, healthy brains, and brains of persons with other neurologic diseases. Specific areas from formalin-fixed, paraffin-embedded brain tissue samples were isolated by laser microscope. DNA was extracted from laser microdissected brain material, and HHV-6 genomic sequences were amplified by nested polymerase chain reaction. We analyzed 44 NAWM samples and 64 MS plaques from 13 patients with MS, 46 samples from 13 patients with non-MS neurologic disorders, and 41 samples from 12 healthy control brains. Of the 44 NAWM samples, 7 (15.9%) were positive for HHV-6 DNA sequences, versus 37 (57.8%) of 64 MS plaques (P<.0005). HHV-6 DNA was detected in 10 (21.7%) of 46 samples from patients with non-MS neurologic disorders and in 11 (26.8%) of 41 samples from patients without known neurologic disease. Although the frequency of HHV-6 DNA did not differ significantly by sample type, HHV-6 DNA was significantly more common in MS plaques, suggesting that HHV-6 may play a role in MS pathogenesis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.277
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations134
Published2003
Admission routes1
Has abstractyes

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